Methods of and devices for adaptive bit rate, abr, video resolution shaping of a video stream in a telecommunications system

ABSTRACT

A method of supporting Adaptive Bit Rate, ABR, video resolution shaping of a video data stream of a video session transferred by a User Plane Function, UPF, in a Service Based Architecture, SBA, domain. The video resolution shaping is performed by the UPF implementing a Reinforcement Learning Agent, RLA, operating with an observation space having a determined video resolution of a received video data stream, a reward space having a reward referring to a required video resolution, and an action space having video resolution shaping levels to be applied at the received video data stream. Complementary methods and devices for performing such a method in an SBA domain deployed in a telecommunications system are disclosed.

TECHNICAL FIELD

The present disclosure generally relates to the field of video streamingand, more specifically, to Adaptive Bit Rate, ABR, video resolutionshaping of a video stream in a Service Based Architecture, SBA, domainin a core network of a telecommunications system, such as a FifthGeneration, 5G, telecommunications system.

BACKGROUND

The Fifth Generation, 5G, telecommunications core network architectureis an example of a Service Based Architecture, SBA, in which NetworkFunctions, NF, provide one or multiple services to entities requiringtelecommunications services from a particular NF. In turn, an NF mayalso request telecommunications services from another NF, for example.The NFs of the Core Network, CN, are self-contained functionalities thatcan be modified and updated in an isolated manner, i.e. withoutaffecting other NFs.

Control and User Plane Separation, CUPS, enables a flexible placement ofthe separated control plane and user plane functions for supportingdiverse deployment scenarios, such as a central or distributed userplane function.

In the Fifth Generation telecommunication network, 5G, CUPS refers toSession Management Function, SMF, and User Plane Function, UPF, networkfunctions, and to the N4 reference point between SMF and UPF, which isbased on Packet Forwarding Control Protocol, PFCP.

The SMF controls the packet processing in the UPF by establishing,modifying or deleting PFCP session contexts and by adding, modifying ordeleting Packet Detection Rules, PDRs, Forwarding Action Rules, FARs,Quality of service Enforcement Rules, QERs, Usage Reporting Rules, URRs,and/or Buffering Action Rule, BAR, per PFCP session context, whereby anPFCP session context may correspond to an individual Protocol Data Unit,PDU, session or a standalone PFCP session, not tied to any PDU session.

Following the packet forwarding model disclosed in the Third GenerationPartnership Project, 3GPP, standard 29.244, “Interface between theControl Plane and the User Plane nodes”, the contents of which areincluded herein by reference, each PDR contains Packet Data Information,PDI, that are one or more match fields against which incoming packetsare matched, and may be associated with the following rules providing aset of instructions to apply to packets matching the PDI:

one FAR, which contains instructions related to the processing of thepackets, specifically forward, duplicate, drop or buffer the packet withor without notifying the Control Plane, CP, function about the arrivalof a DL packet;

zero, one, or more QERs, which contain instructions related to the QoSenforcement of the traffic;

zero, one, or more URRs, which contain instructions related to trafficmeasurement and reporting.

Adaptive Bit Rate, ABR, streaming protocols such as Apple® HypertextTransfer Protocol, HTTP, live stream, HLS, Microsoft® Smooth Streaming,MSS, Adobe® HTTP Dynamic Streaming, HDS, or Dynamic Adaptive Streamingover HTTP, DASH, comprise the latest generation of video streamingprotocols. They natively support delivery optimization techniques thatare handled by the end points, i.e. User Equipment, UE, and videoservers. Although the several ABR protocols have some differences, theyhave the following characteristics in common:

Media streams are encoded beforehand, so that several versions, withdifferent encodings, of the same video can exist and are referenced bythe so called “descriptor file”, each version with its correspondingidentifier/locator, such as a Uniform Resource Locator, URL.

Each of the video versions is partitioned in so called “chunks” or“segments”, i.e. portions of the video data of a certain duration inseconds. The available chunks are also described in the descriptor file,so that the UE can make requests for specific chunks along with theabove-mentioned quality identifier.

Before the actual video delivery takes place, the video server sends tothe UE the “descriptor file” defining the characteristics such as theformat, encoding, duration and listing of differentqualities/characteristics in which the video is available, identified bydifferent descriptors/locators, for example different URLs.

The UE parses the descriptor file and selects videoquality/characteristics, i.e. format, encoding, etc. among those presentin the descriptor file, based on the network conditions as perceived bythe UE and terminal characteristics.

The UE then requests video chunks by means of HTTPS GET messages, whichmay possibly be encrypted, using the appropriate URL indicating thevideo quality and the specific chunks the UE wants to fetch. The UEstores and reproduces the received chunks from a local buffer.

The video quality can also be dynamically reselected during a videosession, using different URLs in the HTTPS GET messages. In this way,video quality can degrade gracefully when the terminal UE cannot keep upwith higher bitrates for any reason such as when, for example, thetransmission resources of the network are overloaded and cannot provideenough bandwidth to the UE, and/or the processing or playing resourcesof the UE are overloaded.

ABR shaping is a technique to control the quality of an ABR video flowor video data stream. It is based on forcing a certain bit rate or“shaping level”, for example by means of throttling mechanisms, to theABR video traffic, which causes the video client, i.e. the UE, to switchthe video resolution to meet the imposed bit rates.

There is no direct relation between bit rates and the video resolutionssince different codecs can be used, and different parts of the videosrequire disparate bit rates. Besides, each video application mayimplement its own resolution selection algorithms, based on the networkconditions observed.

A typical ABR shaping implementation consists of the following modules.

A resolution estimation or prediction module, that estimates theresolution of a video session. A video is divided into chunks thatcorrespond to a portion of the video of a certain duration (around 10sec). The resolution estimation is provided for each chunk. Resolutionlevels can be measured, for example, in low/med/high categories,240p/360p/480p, where p denotes progressive scan and the number denotesthe number of pixels. The resolution estimation module requires acertain number of subsequent chunks to estimate the resolution of avideo stream with acceptable accuracy. If there are not enough chunksavailable, no estimation is provided.

A shaping level decision module, that decides about the shaping level toapply to the video data stream of a video session. This module takes theresolution estimation provided by the resolution estimation module. Ashaping level is a maximum bit rate value. The shaping level to applydepends on the required or wanted resolution for the video session andthe resolution estimation. The required resolution value is staticallyconfigured on a per user basis.

A default shaping level is applied when no estimation is provided by theresolution estimation module, i.e. when there are not enough videochunks to estimate the resolution, for example. For each possible wantedresolution option, a default shaping level is statically preconfigured.

A shaping enforcement module, to apply or enforce the decided shapinglevel. This module takes the shaping level decided by the shaping leveldecision module, and applies the shaping level to the video data streamof the video session.

The known ABR shaping solution entails the following problems:

The resolution estimation module needs several video chunks (for examplearound 3-5 chunks) to provide an accurate estimation of the videoresolution of a received video data stream. During this time, defaultshaping level is applied.

The default shaping level is statically configured. Its value isintended to force the required resolution. However, the network andvideo traffic characteristics change, such that the static value may beoutdated and may be forcing a different resolution than required.

The video resolution is selected by the video application in the UE.However, it is not certain that all the video applications implement thesame resolution selection algorithms, different versions of the samevideo application may implement different algorithms. Accordingly,different resolutions can be selected by different video applicationsunder the same network conditions, which makes a static default shapinglevel a non-optimal solution.

For a certain video resolution, the video chunks can be encoded usingdifferent video codecs. Each codec entails a different bit rate. Using adefault shaping level per wanted resolution and static heuristicalgorithms for the shaping level decision are therefore non-optimalapproaches, since they cannot adapt to video traffic that uses differentcodecs.

The value of the default shaping level may be obtained, for example, bymeans of laboratory tests, based on historical data or video captures,etc. To check if this value is still valid and up-to-date, the sameprocess has to be repeated, which is slow and costly.

The video resolution may change during the first chunks of the video.The new video chunks are sent through a new video flow, which causes theprevious chunks to be discarded. If this occurs, more video chunks areneeded to estimate the video resolution, and the effects of a wrongdefault shaping level would be more pronounced.

Only when the resolution estimation or prediction module has processedenough chunks and estimated the video resolution, the shaping leveldecision module is able to correct the shaping level in case theresolution of a transferred video data stream of a video session has notthe wanted resolution. It is noted that, although the default shapinglevel was provoking a wrong video resolution, this default value ismaintained.

The shaping level decision module is based on heuristic algorithms,which lead to non-optimal decisions. Some of them face convergenceissues as well. For example, an algorithm that increases/decreases theshaping level by a constant delta and observes the results, may take along time until the wanted resolution is achieved.

SUMMARY

The present disclosure has for its object to implement improved AdaptiveBit Rate, ABR, shaping support of a video stream in a Service BasedArchitecture, SBA, domain in a core network of a telecommunicationssystem, and more particular to provide an autonomous solution to adaptchanging network and video characteristics for individual users.

In a first aspect of the present disclosure, there is presented a methodof supporting Adaptive Bit Rate, ABR, video resolution shaping of avideo data stream of a video session transferred by a User PlaneFunction, UPF, in a Service Based Architecture, SBA, domain deployed ina telecommunications system.

The method comprises the steps of determining, by the UPF, a videoresolution of a video data stream received by the UPF; establishing, bythe UPF, based on the determined video resolution, a required videoresolution and a Maximum Bit Rate, MBR, of the video session, a videoresolution shaping level to be applied at the received video datastream, for achieving the required video resolution of the videosession; applying, by the UPF, the video resolution shaping on thereceived video data stream, and transferring, by the UPF, the receivedvideo data stream at which the video resolution shaping is applied.

The step of establishing the video resolution shaping is performed bythe UPF implementing a Reinforcement Learning Agent, RLA, operating withan observation space comprising the determined video resolution, areward space comprising a reward referring to the required videoresolution, and an action space comprising video resolution shapinglevels to be applied at the received video data stream.

Reinforcement learning is a type of machine learning where an RLA learnshow to behave in an environment by performing actions and observing theresults. When performing an action, the RLA receives a reward, whichindicates whether the environment is in a desirable state or not. TheRLA executes algorithms that learn to take actions that maximize acumulative reward in the long term.

In machine learning, the environment is typically modeled as a MarkovDecision Process, MDP. MDP uses a finite set of states and a finite setof actions that lead to state changes. A reinforcement learning agentinteracts with its environment in discrete time steps. At each timestep, the environment is in some State (St) and sends an observation ofthis state along with a current Reward (Rt) to the RLA. Then the RLA maychoose an Action (At) that is available in that state. Then theenvironment responds at the next time step by moving into a new state(St+1) and giving the agent a corresponding reward (Rt+1), etc.

All the possible actions the RLA can take is named “action space”, andall the possible states of the environment is named “observation space”.Iterating over this process and observing the rewards, the RLA learnsoptimal policies that map states to actions in such a way that thecumulative reward of the actions gets maximized.

In operation the RLA switches between two modes and will find a balancebetween them: exploration (of uncharted territory) and exploitation (ofcurrent knowledge):

Exploration—The RLA takes actions that do not follow the optimalpolicies. For example, selecting actions randomly, using heuristicalgorithms, or using more complex and optimized methods such asepsilon-greedy.

Exploitation—The RLA takes actions according to the optimal policiesthat have been learned during the exploration phase.

The RLA may have full observability of the whole environmental state ormay have partial observability of the environment state. This means thatdifferent RLAs acting over the same environment may receive differentstate information from it.

By applying RLA in the UPF of an SBA domain in a core network of atelecommunications system, an improved and autonomous solution isprovided to adapt the video resolution of a data stream of a videosession transferred by the UPF from a source of video data to a user ofthe video data, such as a User Equipment, UE, or client server, insupport of ABR streaming protocols . In particular, the presentdisclosure provides for ABR support in changing network and videocharacteristics for individual users.

The disclosed solution avoids the need for a statically configureddefault shaping level for a given wanted resolution. Instead, the RLA isable to learn the optimal policies that map the different shaping levelsto the required or wanted video resolution and the determined resolutionof a video data stream of a respective video session. The solution isapplicable to operate with multiple UPF in an SBA domain and allows toadapt to changing network and video traffic characteristics for eachindividual UPF.

The RLA agent continuously, autonomously learns the optimal policies forthe shaping level decisions, such that no human intervention is needed.The solution may also work for encrypted video traffic.

According to an embodiment of the present disclosure, the videoresolution of the received video data stream is determined, by the UPF,by computing a video resolution state of a video chunk of the receivedvideo data stream and an associated reward based on the computed videoresolution state and the required video resolution.

In an exemplary embodiment, the reward is computed, by the UPF,additionally based on at least one of overall network load, anestimation of Quality of Experience, QoE, of the video session, videosession throughput at the UPF, estimated resolutions of previous videochunks, and network transmission parameters.

The video resolution state and the associated reward may be computed, byan implementation of the RLA, on a chunk by chunk basis. If no videoresolution can be computed, an “unestimated” resolution state may beapplied, for example.

In an embodiment of the present disclosure, a video resolution shapinglevel is applied, by the UPF, selected by the RLA from the action spacebased on the video resolution state, the associated reward, and a levelof operation of the RLA, the video resolution shaping level comprising abit rate of the video data stream to be transferred by the UPF. Thelevel of operation of the RLA comprises, among others, the policieslearned by the RLA, whether same is in exploration or exploitation mode,and the set of possible actions, i.e. the size of the level shapingaction space.

Those skilled in the art will appreciate that the RLA, in the presentdisclosure, may apply multiple commercially available algorithms forestimating video resolution, reward computation, and for derivingshaping levels. The choice of algorithm is a mere implementation aspect,and does not form part of the present disclosure. Further, a UPF maycomprise a plurality of RLA, operating with the same or mutuallydifferent processing algorithms, for example.

For operating the UPF in accordance with the present disclosure in anSBA domain comprising a Session Management Function, SMF, the UPFassociates with the SMF by transmitting, to the SMF, a Packet ForwardingControl Protocol, PFCP, Association Setup Response message indicatingthat the UPS supports RLA based ABR video resolution shaping.

The UPF receives, from the SMF, a Packet Forwarding Control Protocol,PFCP, Association Setup Response message comprising Control Plane, CP,features including RLA based ABR video resolution shaping supported bythe SMF.

In a further embodiment, when a user establishes a Protocol Data Unit,PDU, video session, that is an association between a UE and a datanetwork for exchanging a video data stream, the UPF receives, from theSMF, a PFCP Session Establishment Request message comprising a PacketDetection Rule, PDR, providing data packet matching rules of a videodata stream of a video session to be transferred by the UPF applying RLAbased ABR video resolution shaping, the PDR comprising a videoapplication identifier, video application-ID, and a correspondingQuality of Service, QoS, Enforcement Rule, QER, comprising a requiredvideo resolution and an MBR of the video session, and wherein the UPFtransmits a PCFP Session Establishment Response message to the SMF.

That is, in this implementation, the SMF selects a UPF supporting theRLA based ABR shaping feature. The SMF conveys the requested or wantedresolution and MBR to the UPF as part of the N4 session establishmentprocedure within a QER. The wanted or requested solution may include acategorical value such as 240p/480p/1080p/ . . . , for example.

In the case of an update of or modification in a continuing session, inan embodiment of the present disclosure, in line with sessionestablishment, the UPF receives, from the SMF, a PFCP SessionEstablishment Modification message comprising a PDR providing a modifiedQER, comprising a required video resolution and MBR of the videosession.

In an embodiment, the method further comprises the steps of receiving,by the UPF, a video data stream of a video session to be transferred bythe UPF, the video data stream comprising a video application ID;determining, by the UPF, based on the video application ID, that RLAbased ABR video resolution shaping has to be applied at the receivedvideo data stream; determining, by the UPF, that a first data packet ofthe video data stream of the video session is received, and generatingand storing a video session identifier, video session-ID, for the videosession in relation to the video application-ID; establishing, by theRLA of the UPF, the video resolution shaping for the video session basedon the video session-ID, and wherein if the UPF comprises a plurality ofRLA each operative for a different QER, the video session-ID is mappedto a respective RLA.

The video application-ID identifies a particular video application,which may also relate to a particular video data source or provider, forexample, and whether RLA based ABR is to be applied by the UPF intransferring the particular video data stream. Based on a received firstdata packet of the respective video data stream, a video session-ID isgenerated, by the UPF, among others for operating the RLA with theapplicable settings for the video session and for mapping the videosession to a particular RLA of the UPF, if applicable.

In a second aspect of the present disclosure, there is presented amethod of supporting Adaptive Bit Rate, ABR, video resolution shaping ofa video data stream of a video session transferred by a User PlaneFunction, UPF, in a Service Based Architecture, SBA, domain deployed ina telecommunications system, from the perspective of a SessionManagement Function, SMF, comprised by the SBA domain.

The method according to the second aspect of the present disclosurecomprises the steps of receiving, by the SMF, a Packet ForwardingControl Protocol, PFCP, Association Setup Response message from the UPF,indicating that the UPF supports Reinforcement Learning Agent, RLA,based ABR video resolution shaping, and transmitting, by the SMF, to theUPF, a Packet Forwarding Control Protocol, PFCP, Association SetupResponse message comprising Control Plane, CP, features including RLAbased ABR video resolution shaping supported by the SMF.

With this method, the or each UPF and SMF in an SBA domain areassociated, indicating that a UPF supports RLA based ABR videoresolution shaping in accordance with the present disclosure.

According to an embodiment, wherein the SBA domain further comprises anAccess and Mobility Management Function, AMF, and a Policy ControlFunction, PCF, and the method further comprises the steps of receiving,by the SMF, a video session establishment request message from the AMF,the message including a user identifier, user-ID; querying, by the SMF,the PCF for Policy and Charging Control, PCC, rules relating to theuser-ID; receiving, by the SMF, in reply to the querying, a PCC rulecomprising a video application identifier, video application-ID, and acorresponding Quality of Service, QoS, Enforcement Rule, QER, comprisinga required video resolution and an MBR of the video session; selecting,by the SMF, based on the received PCC rule, a UPF supporting RLA basedABR video resolution shaping, for transferring a video data stream ofthe video session; transmitting, by the SMF, to the selected UPF, a PFCPSession Establishment Request message comprising a Packet DetectionRule, PDR, providing data packet matching rules of the video data streamof the video session to be transferred by the UPF applying RLA based ABRvideo resolution shaping, the PDR comprising the video application-ID,and corresponding QER of the video session, and receiving, by the SMF, aPCFP Session Establishment Response message from the UPF.

When a user establishes a PDU session, the PCF provides the required orwanted resolution and an MBR as part of the PCC rules. The SMF selects aUPF supporting the RLA ABR shaping feature and the SMF conveys thewanted resolution and MBR to the selected UPF as part of the N4 sessionestablishment procedure within a QER.

In a third aspect of the present disclosure, there is presented a methodof supporting Adaptive Bit Rate, ABR, video resolution shaping of avideo data stream of a video session transferred by a User PlaneFunction, UPF, in a Service Based Architecture, SBA, domain deployed ina telecommunications system, wherein the SBA domain further comprises aPolicy Control Function, PCF, and Session Management Function, SMF.

The method according to the third aspect comprises, viewed from theperspective of the PCF, the steps of receiving, by the PCF, a query ofthe SMF for Policy and Charging Control, PCC, rules relating to auser-ID; transmitting, by the PCF, in reply to the query, a PCC rulecomprising a video application identifier, video application-ID, and acorresponding Quality of Service, QoS, Enforcement Rule, QER, comprisinga required video resolution and an MBR of the video session forsupporting Reinforcement Learning Agent, RLA, based ABR video resolutionshaping by the UPF.

In a fourth aspect of the present disclosure, there is presented a UserPlane Function, UPF, in a Service Based Architecture, SBA, domaindeployed in a telecommunications system, the UPF implementing aReinforcement Learning Agent, RLA, operating with an observation space,a reward space, and an action space comprising video resolution shapinglevels to be applied at the received video data stream supportingAdaptive Bit Rate, ABR, video resolution shaping of a video data streamof a video session to be transferred by the UPF, in accordance with anyof the embodiments presented in the first three aspects of the presentdisclosure.

According to an embodiment of the fourth aspect of the presentdisclosure, the UPF comprises a resolution estimation module, arrangedfor determining a video resolution of video chunks of a video datastream received by the UPF, a shaping level decision module, arrangedfor establishing a video resolution shaping level to be applied at thereceived video data stream, based on the determined video resolution, arequired video resolution and an MBR, of the video session, forachieving the required video resolution of the video session, and ashaping enforcement module, arranged for applying the established videoresolution shaping level on the received video data stream for transferby the UPF, wherein the observation space and the reward space of theRLA operate in conjunction with the resolution estimation module, andthe action space operates in conjunction with the shaping level decisionmodule.

In a fifth aspect of the present disclosure, there is presented aService Based Architecture, SBA, domain deployed in a telecommunicationssystem, comprising a User Plane Function, UPF, in accordance with thefourth aspect of the present disclosure, and at least one of a SessionManagement Function, SMF, and a Policy Control Function, PCF, arrangedfor operating in accordance with the various embodiments presentedherein.

In a sixth aspect of the present disclosure, computer program productsare provided, comprising a computer readable storage medium, storinginstructions which, when executed on at least one processor operative inone of a UPF, SMF, PCF of an SBA domain, cause the at least oneprocessor to carry out processing steps for performing the stepsdisclosed in a respective one of the method according to the first,second and third aspect of the present disclosure.

It will be appreciated that the entities and modules disclosed may beimplemented as separate hardware and/or software modules and entities,and controlled by or executed in a processor or the like.

The above mentioned and other features and advantages of the disclosurewill be best understood from the following description referring to theattached drawings. In the drawings, like reference numerals denoteidentical parts or parts performing an identical or comparable functionor operation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates a part of a reference architecture of aFifth generation, 5G, telecommunication systems according to the priorart.

FIG. 2 schematically illustrates an Adaptive Bit Rate, ABR, solutionaccording to the present disclosure.

FIG. 3 schematically illustrates part of a method according to thepresent disclosure.

FIG. 4 schematically illustrates part of a method according to thepresent disclosure.

FIG. 5 schematically illustrates a method according to the presentdisclosure.

DETAILED DESCRIPTION

Embodiments contemplated by the present disclosure will now be describedmore in detail with reference to the accompanying drawings. Otherembodiments, however, are contained within the scope of the subjectmatter disclosed herein. The disclosed subject matter should not beconstrued as limited to only the embodiments set forth herein; rather,the illustrated embodiments are provided by way of example to convey thescope of the subject matter to those skilled in the art.

FIG. 1 schematically illustrates part of the reference architecture 1 ofa fifth generation, 5G, Service Based Architecture, SBA,telecommunication network, according to the prior art. The 5G systemarchitecture comprise the following Network Functions, NFs:

-   -   Access and Mobility Management Function, AMF, 8    -   Network Exposure Function, NEF, 3    -   Policy Control Function, PCF, 6    -   Session Management Function, SMF, 9    -   Unified Data Repository, UDR, 2    -   User Plane Function, UPF, 10    -   Application Function, AF, 5    -   Network Data Analytics Function, NWDAF, 4    -   Charging Function, CHF, 7.

A functional description of these network functions is specified inclause 6 of the Third Generation Partnership Project, 3GPP, standard23.501, “System Architecture for the 5G system”, the contents of whichare included herein by reference.

FIG. 2 schematically illustrates an Adaptive Bit Rate, ABR, solution 20according to the present disclosure. Here, the network nodes PCF 6, SMF9 and UPF 10 are shown, and how these nodes collaborate with each otherto achieve a desired result according to the present disclosure. Thetraditionally known functions and services offered by the UPF 10 areimplemented by the UPF logic 21. This may be a physical or logicalentity. The UPF 10 further comprises a shaping level decision module 24,a resolution estimation module 22 and a shaping enforcement module 28.The person skilled in the art appreciates that these entities may eitherbe physical or logical entities implemented within the UPF 10.

A Reinforcement Learning Agent, RLA, 25 collocated in the shaping leveldecision module 24 takes decisions on the shaping level to apply to avideo session 32. The resolution estimation module 22 acts as theReinforcement Learning, RL, environment 23, sending to the shaping leveldecision module 24 the corresponding states 26 and rewards 27. Therequired or wanted resolution 29 is provided by PCF 6 on a per user andper video application basis. The wanted resolution 29 reaches UPF 10 viaSMF 9 upon PDU session establishment.

The wanted resolution 29 is conveyed 30 to the resolution estimationmodule 22 so that it can be used as an input to compute the reward. Butthe reward may also be computed as a function of many other parameters.The wanted resolution 29 is conveyed 31 to the shaping level decisionmodule 24, so that it may be used as input to determine the shapinglevel space i.e. the set of shaping levels to choose from the so-calledaction space.

The resolution estimation module 22 processes video chunks from thevideo traffic 32 and tries to estimate the video resolution. For eachprocessed video chunk, the resolution estimation module 22 sends to theshaping level decision module 24 the determined resolution state 26 ,for example, an estimated video resolution or “unestimated resolution”state, and the associated reward 27, for example computed based on theestimated resolution and the wanted resolution.

The RLA 25 in the shaping level decision module 24 takes the shapinglevel decisions based on the wanted resolution, the received state, thereceived reward, the set of possible actions, whether it's onexploration or exploitation mode, the learned policies, etc. andprovides the decision to the shaping enforcement module 28.

In FIG. 3, method 40 schematically illustrates an association procedurebetween the UPF 10 and the SMF 9. When the UPF 10 is deployed in thenetwork, it first needs to associate to a SMF 9. To that extent the UPF10 sends to SMF 9 a PFCP Association Setup Request message 41 includingthe User Plane, UP, function features it supports. It also includes theindication of a new feature: the support of the Reinforcement Learning,RL, based Adaptive Bit Rate, ABR, shaping.

In turn the SMF 9 replies to the association request 41 with a PFCPAssociation Setup Response message 42 including the Control Plane, CP,function features it supports. It also includes the indication of a newfeature: the support of the Reinforcement Learning based ABR shaping.

It may be noted that step 41 may be triggered by the UPF 10 or the SMF9. In case it is triggered by SMF 9, the SMF 9 sends the associationrequest message to UPF 10 including the CP features and the response theUP features.

In FIG. 4, method 50 depicts the PDU session establishment procedure.The UE 11 sends a PDU session establishment request message to AMF (notshown), and AMF relays 51 it to SMF 9. The message may include theUser-ID. The SMF 9 queries 52 the PCF 6 to get the policy rules, whereinthe query message may include the User-ID as a parameter.

The PCF 6 responds 53 to SMF 9 with the policy rules for that specificuser. The policy rules include a wanted resolution and a Maximum BitRate, MBR, along with the corresponding video Application-ID, forexample App-ID=YouTube™. The wanted resolution is a categorical value,e.g. high/medium/low, or 240p/480p/1080p, . . . , etc.

When SMF 9 receives the wanted resolution in a PCC rule it knows thisrelates to the RL based ABR shaping feature in the UPF 10, accordinglySMF 9 selects 54 a UPF 10 supporting this feature for the user. The SMF9 sends 55 to UPF 10 a PFCP Session Establishment Request messagecomprising A Packet Detection Rule, PDR, indicating the packet matchingrules and a Quality of Service Enforcement Rule, QER, including thewanted resolution and Maximum Bit Rate, MBR. It may be understood, bythe skilled person, that a message for the update or modification of asession—i.e. session establishment modification—is equivalent to thisstep.

The UPF 10 sends a PFCP Session Establishment Response message 56 backto SMF 9. The PDU session establishment procedure is completed 57.

FIG. 5 schematically depicts the procedures and reinforcement learningmechanisms that take place for each video session. In a first step ofthe method 60, video traffic is sent 71 from the video server 61 towardsthe UE 11. The video traffic reaches UPF 10.

The video packets match 72 a PDR associated to a QER that contains thewanted resolution and MBR. UPF 10 recognizes then that this trafficbelongs to a video application that uses the RL-based ABR shapingfeature. If the packet is the first packet of the video session, steps73-80 take place.

UPF 10 generates 73 a video session Identifier, ID, for the videosession. Then it stores the video session-ID—IP 5 tuple mapping to beable to derive the video-session-ID for subsequent packets. The videosession-ID and wanted resolution are conveyed 74 to the resolutionestimation module 22. In a step 75, the resolution estimation module 22configures the state and reward computation algorithms for the Videosession-ID and wanted resolution. It will be understood by the skilledperson that multiple algorithms may be used and what specific algorithmsthe resolution estimation module 22 uses is implementation-specific andis not within the scope of the present disclosure.

The video-session-ID, wanted resolution, and MBR are conveyed 76 to theshaping level decision module 24. If multiple separate RLAs are used foreach different wanted resolution, for example, the shaping leveldecision module 24 selects 77 the corresponding RLA and stores the videosession-ID RLA mapping to find what RLA handles a certain videosession-ID. The RLA configures 78 the shaping level space based on thewanted resolution and MBR for the video session-ID.

The shaping level space is the set of different shaping levels that canbe chosen for the video session-ID, i.e. the so-called action space inreinforcement learning. The shaping level space is derived based on thewanted resolution and MBR. As an example, the step between shapinglevels can be higher if the wanted resolution is very high, and lower ifthe wanted resolution is low. And, of course, there may not be a shapinglevel bigger than the MBR. It may be understood that the specificalgorithms that may be used to derive the shaping level space is animplementation aspect, and does not belong to the scope of the presentdisclosure.

In step 89, the RLA decides the shaping level based on the wantedresolution, the set of possible actions—i.e. the shaping level actionspace, and based on whether it is on exploration or exploitation mode,the learned policies, etc. The shaping level decision module 24 sends 80to the shaping enforcement module 28, the video session-ID and thedecided shaping level.

The UPF 10 takes the video session-ID corresponding to the video sessionand adds 81 it as metadata in the video traffic. The video traffic withthe video session-ID as metadata is sent 82 to the resolution estimationmodule 22. The resolution estimation module 22 analyzes 83 the videopackets, extracts the relevant parameters, and may also classify thepackets into categories, for example by using Machine Learningmechanisms, and then stores all the extracted information beforeforwarding or transferring the packet to the destination, i.e. a UE orclient server, or the like.

The video traffic with the video session-ID as metadata is sent 84 tothe shaping enforcement module 28. The shaping enforcement module 28enforces 85 the corresponding shaping level bit rate to the videosession. It also removes the video session-ID metadata before forwarding86 the packets towards the UE 11.

The video traffic sent from the UPF 10 reaches the video application inthe UE 11. After analyzing and forwarding out the video packets in steps83, 84, the resolution estimation module 22 checks 87 whether a videochunk has been fully transmitted. If the resolution estimation moduledetects that a video chunk has been fully transmitted, the followingsteps 88-93 take place, else the process ends here with step 87.

The resolution estimation module 22 estimates 88 the resolution of thevideo chunk and based on the estimated resolution, the resolutionestimation module 22 determines 88 the state and computes the reward.

As the simplest approach, the state can be the estimated resolutionitself, and the reward can be computed as the difference between theestimated resolution and wanted resolution. But more complex computationmodels may be used, considering other parameters such as previousestimated resolutions, the current video session throughput, and otherparameters extracted in the packet analysis.

The reward may be also computed using other UPF information, forexample, by considering the overall network load status, wherein acongested network would mean a low reward, transmission parameters,etc., and/or the real-time user's QoE estimation if available in the UPFby means of analytics processes, wherein a low QoE would mean a lowreward.

The resolution estimation module 22 sends 90 to the shaping leveldecision module 24 , the video-session-ID, the state and the computedreward. Based on the received state and reward, the RLA learns 91 theeffect of the past shaping level decisions. The learning phase basicallymeans that the RLA learns how to map the states to the actions in anoptimal way, usually by trying to maximize the reward of the actions.

In a step 92, the RLA in the shaping level decision module 24 decidesthe shaping level based on the received state and reward, the wantedresolution, the set of possible actions—i.e. the shaping level actionspace, whether it is on exploration or exploitation mode, the learnedpolicies, etc.

The shaping level decision module 24 sends 93 to the shaping enforcementmodule 28 the video session-ID and the decided shaping level. Then thisshaping level is applied for the subsequent traffic of the videosession.

When it comes to the exploration and exploitation phases of the RLA, itis important to highlight that the exploration phase can take place in acontrolled environment, for example in a laboratory, and not in aproduction environment. Other possibility is to use existing productiondata to pre-train the RLA. The RLA can be deployed in the productionenvironment already trained to avoid an initial extensive explorationphase in production, which may not be desirable.

Other variations to the disclosed examples can be understood andeffected by those skilled in the art of practicing the claimeddisclosure, from a study of the drawings, the disclosure, and theappended claims. In the claims, the word “comprising” does not excludeother elements or steps, and the indefinite article “a” or “an” does notexclude a plurality. A single processor or other unit may fulfil thefunctions of several items recited in the claims. The mere fact thatcertain measures are recited in mutually different dependent claims doesnot indicate that a combination of these measures cannot be used toadvantage. A computer program may be stored/distributed on a suitablemedium, such as an optical storage medium or a solid-state mediumsupplied together with or as part of other hardware, but may also bedistributed in other forms, such as via the Internet or other wired orwireless telecommunication systems. Any reference signs in the claimsshould not be construed as limiting the scope thereof.

The present disclosure is not limited to the examples as disclosedabove, can be modified and enhanced by those skilled in the art beyondthe scope of the present disclosure as disclosed in the appended claimswithout having to apply inventive skills.

1. A method of supporting Adaptive Bit Rate, ABR, video resolutionshaping of a video data stream of a video session transferred by a UserPlane Function, UPF, in a Service Based Architecture, SBA, domaindeployed in a telecommunications system, the UPF being a network node,the method comprising: determining, by the UPF, a video resolution of avideo data stream received by the UPF by computing a video resolutionstate of a video chunk of the received video data stream in which thevideo resolution state is an estimated resolution of the video chunk;establishing, by the UPF, based on the determined video resolution, arequired video resolution and a Maximum Bit Rate, MBR, of the videosession, a video resolution shaping level to be applied at the receivedvideo data stream, for achieving the required video resolution of thevideo session; applying, by the UPF, the video resolution shaping on thereceived video data stream; transferring, by the UPF, the received videodata stream at which the video resolution shaping is applied;establishing the video resolution shaping being performed by the UPFimplementing a Reinforcement Learning Agent, RLA, operating with anobservation space comprising the determined video resolution, a rewardspace comprising a reward based on the computed video resolution stateand the required video resolution, the reward being computed, by theUPF, additionally based on overall network load; and an action spacecomprising video resolution shaping levels to be applied at the receivedvideo data stream, the video resolution shaping level comprising a bitrate of the video data stream to be transferred by the UPF, and thevideo resolution shaping level being applied, by the RLA selecting avideo resolution shaping level from the action space based on the videoresolution state, the reward, a level of operation of the RLA, and theMBR of the video session.
 2. (canceled)
 3. The method according to claim1, wherein the reward is computed, by the UPF, additionally based on atleast one of, an estimation of Quality of Experience, QoE, of the videosession, video session throughput at the UPF, estimated resolutions ofprevious video chunks, and network transmission parameters. 4.-12.(canceled)
 13. A User Plane Function, UPF, in a Service BasedArchitecture, SBA, domain deployed in a telecommunications system, theUPF being a network node, the UPF implementing a Reinforcement LearningAgent, RLA, operating with an observation space, a reward space, and anaction space comprising video resolution shaping levels to be applied atthe received video data stream supporting Adaptive Bit Rate, ABR, videoresolution shaping of a video data stream of a video session to betransferred by the UPF, the UPF being configured to: determine, by theUPF, a video resolution of a video data stream received by the UPF bycomputing a video resolution state of a video chunk of the receivedvideo data stream in which the video resolution state is an estimatedresolution of the video chunk; establish, by the UPF, based on thedetermined video resolution, a required video resolution and a MaximumBit Rate, MBR, of the video session, a video resolution shaping level tobe applied at the received video data stream, for achieving the requiredvideo resolution of the video session; apply, by the UPF, the videoresolution shaping on the received video data stream; transfer, by theUPF, the received video data stream at which the video resolutionshaping is applied; establishing the video resolution shaping beingperformed by the UPF implementing a Reinforcement Learning Agent, RLA,operating with an observation space comprising the determined videoresolution, a reward space comprising a reward based on the computedvideo resolution state and the required video resolution, the rewardbeing computed, by the UPF, additionally based on overall network load;and an action space comprising video resolution shaping levels to beapplied at the received video data stream, the video resolution shapinglevel comprising a bit rate of the video data stream to be transferredby the UPF, and the video resolution shaping level being applied, by theRLA selecting a video resolution shaping level from the action spacebased on the video resolution state, the reward, a level of operation ofthe RLA, and the MBR of the video session.
 14. (canceled)
 15. (canceled)16. The UPF according to claim 13, wherein the reward is computed, bythe UPF, additionally based on at least one of, an estimation of Qualityof Experience, QoE, of the video session, video session throughput atthe UPF, estimated resolutions of previous video chunks, and networktransmission parameters.